Microsoft Research

Scientific Publishing in a Connected, Mobile World 'New tools for content development and new distribution channels creates opportunities for the scientific community, opening new venues for

Microsoft Research – 12 августа 2016, 3:11

DonΓÇÖt despair that our political system is hopelessly gridlocked by old ideas and entrenched interests.

A primary motivation for work within my group is the notion of autonomous agents that can interact, robustly over the long term, with an incompletely known environment that continually changes.

Knowledge-based security policies are those which specify a threshold on an adversary's knowledge about secret data.

The system of real numbers are defined mathematically as a 'completion' of the rational numbers. But this is not the only way to do it!

Decisions in information extraction (IE), such as determining the types and relations of entities mentioned in text, depend on each other.

I will discuss some algorithmic problems, old and new, concerning convex bodies in high dimensions.

S V N Vishwanathan (Vishy) and Prateek Jain will offer a 10 week Machine Learning course. It will be an exciting mix between theory and application talks by various guest lecturers.

The goal of cross-domain interoperability is to enable reuse of data and models outside the original context in which these data and models are collected and used and to facilitate analysis and

Cloud computing for science is seeing take-up in many disciplines, but many researchers are skeptical.

Machine Learning Work Shop-Session 5 - Maryam Fazel - 'Recovery of Simultaneously Structured Models by Convex Optimization' The topic of deriving a structured model from a small number of linear

Machine Learning Work Shop-Session 5 - Lin Xiao - 'A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem' We consider the l1-regularized least-squares problem in the context of

Machine Learning Work Shop-Session 4 - Ece Kamar - 'Combining Machine and Human Intelligence in Crowdsourcing' Crowdsourcing has been increasingly popular for gaining programmatic access to human

4:00 ΓÇô 5:20 Session 10 Session 10 ΓÇô Chair: Marc J. van Kreveld Topics By B. Bach, A. Spritzer, E. Lutton, and J.-D. Fekete A. Suk Nivasch, J. Pach, and G. Tardos G.L. Castelli Aleardi, O.

Increasingly, we are confronted with very high dimensional data sets. As a result, methods of avoiding the curse of dimensionality have come to the forefront of machine learning research.

Inference is the hardest part of learning. Learning most powerful models requires repeated intractable inference, and approximate inference often interacts badly with parameter optimization.

Machine Learning Work Shop - Session 3 - Asela Gunawardana - 'Graphical Event Models for Temporal Event Streams' Many phenomena can be described as streams of diverse events in time.

Most machine learning algorithms rely on the assumption that the data is generated by a stochastic process.

Machine Learning Work Shop-Session 1 - Leon Bottou - 'Counterfactual Measurements and Learning Systems' This work shows how to leverage causal inference to understand the behavior of com- plex

Machine Learning Work Shop - Session 2 - Jeff Bilmes - 'Why Submodularity is Important to Machine Learning' It is well known that submodular functions have a set of tight subdifferentials.

Machine Learning Work Shop-Session 3 - Emily Fox - 'Bayesian Nonparametrics for Complex Dynamical Phenomena' Markov switching processes, such as hidden Markov models (HMMs) and switching linear

Machine Learning Work Shop - Session 1 - Carlos Guestrin - 'GraphLab: Large-scale Machine Learning on Natural Graphs' GraphLab: Large-scale Machine Learning on Natural Graphs Today, machine learning

This is Ossama AbdelhamidΓÇÖs final presentation on his internship work at MSR speech group on building and evaluating deep and segmental convolutional neural networks for speech recognition.

The e-Science domain brings together scientists, experts, and engineers to enterprise comprehensive, large-scale data and computational cyberinfrastructures.

The University of Washington eScience Institute is engaged in a number of educational efforts in data science, including certificate programs for professionals, workshops for students in domain

I suggest that there are two distinct branches of eScience, both fundamentally enabled by the explosion of capabilities inherent in the information age.

Since its beginning, the wet lab has been the key driver in biological discovery. Recently, however, more and more science is getting done in dry labs, those where only computational analysis is done.

8:50 ΓÇô 9:00 Opening 9:00 ΓÇô 10:40 Session 4 Chair: Daniel Archambault Speakers: D.Eppsein H. Purchase, J. Hamer, M. N├╢llenburg, and S. G. Kobourov M. A. Bekos and C. Raftopoulou M. A. Bekos, S. G.

S V N Vishwanathan (Vishy) and Prateek Jain will offer a 10 week Machine Learning course. It will be an exciting mix between theory and application talks by various guest lecturers.